store = {}
store['args']={'batch_size': 64, 'scoring_batch_size': 1000, 'test_batch_size': 1000, 'validation_set_size': 1000, 'early_stopping_patience': 5, 'epochs': 30, 'epoch_samples': 5056, 'num_inference_samples': 20, 'available_sample_k': 10, 'num_iterations': 100, 'no_cuda': False, 'name': 'bald_20_534918', 'seed': 534918, 'log_interval': 10, 'type': 'AcquisitionFunction.bald'}
store['iterations']=[]
store['initial_samples']=[35667, 6502, 45437, 49697, 7916, 21136, 27362, 26284, 45184, 29075, 53690, 13940, 33264, 5561, 21478, 23782, 3489, 10973, 21218, 58470]
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.6373, 'nll': 2.519788932800293}, 'chosen_samples': [2748, 25289, 19173, 42805, 42839, 49117, 28395, 7005, 26981, 29904], 'chosen_samples_score': ['1.1841763', '1.2073948', '1.1846606', '1.2107913', '1.2710092', '1.2262758', '1.22367', '1.2563019', '1.2256565', '1.3966008']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.6355, 'nll': 2.069090461730957}, 'chosen_samples': [31814, 44110, 57638, 40164, 31881, 33554, 47615, 54860, 26382, 32182], 'chosen_samples_score': ['0.9799275', '0.98206925', '0.9854377', '0.994109', '1.0261216', '1.0281739', '1.0427217', '1.118276', '1.0742495', '1.0469385']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.7135, 'nll': 1.5010524153709413}, 'chosen_samples': [22583, 44101, 51209, 9799, 11121, 33429, 22641, 7887, 45245, 41544], 'chosen_samples_score': ['1.0687239', '1.071234', '1.0722985', '1.0823017', '1.0773928', '1.082736', '1.0736828', '1.0982318', '1.0906891', '1.1231797']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.7605, 'nll': 1.177311760187149}, 'chosen_samples': [12229, 27199, 12208, 38908, 6898, 8339, 11616, 56348, 39473, 32258], 'chosen_samples_score': ['0.94111097', '0.95055455', '0.95148635', '0.9429156', '0.9461558', '0.94888335', '0.9465054', '0.96829164', '0.94324726', '0.9643799']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.8026, 'nll': 0.9846196711063385}, 'chosen_samples': [7833, 34829, 20257, 54973, 37758, 36268, 10038, 57724, 57876, 37373], 'chosen_samples_score': ['0.9004447', '0.90690565', '0.9118307', '0.91441613', '0.91443515', '0.95418286', '0.9627496', '0.97680855', '0.9260759', '0.97899556']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.7968, 'nll': 0.969130915403366}, 'chosen_samples': [51759, 59390, 51986, 39411, 8833, 4795, 4165, 3719, 42565, 50782], 'chosen_samples_score': ['0.83270365', '0.8428539', '0.83936787', '0.8454492', '0.86069304', '0.8603617', '0.8627847', '0.9012713', '0.91395885', '0.86390036']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.767, 'nll': 1.0666679441928864}, 'chosen_samples': [33222, 49537, 37714, 35051, 45813, 15801, 28373, 49499, 21491, 19942], 'chosen_samples_score': ['0.7703722', '0.7703979', '0.7735214', '0.7787758', '0.7962568', '0.7982547', '0.7889946', '0.7742917', '0.7793597', '0.7805954']})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.8423, 'nll': 0.8177323848009109}, 'chosen_samples': [44853, 8958, 16084, 1001, 41367, 27209, 4694, 18405, 49094, 25315], 'chosen_samples_score': ['0.96194345', '0.96807677', '0.9693824', '0.981783', '0.9747286', '0.9952194', '1.057613', '1.0752316', '1.1038179', '1.0578706']})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.8486, 'nll': 0.8708150148391723}, 'chosen_samples': [59344, 13347, 2880, 22404, 3200, 41553, 15949, 150, 13742, 42317], 'chosen_samples_score': ['0.9140608', '0.9237874', '0.9337607', '0.9359701', '0.943889', '0.93633366', '0.95086825', '0.9587168', '0.96428853', '1.0200486']})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.8528, 'nll': 0.8158496081829071}, 'chosen_samples': [57212, 44013, 46155, 54039, 18739, 13827, 37696, 59339, 29132, 50343], 'chosen_samples_score': ['0.9618091', '0.9715833', '0.9785997', '0.97917116', '1.024794', '1.0002165', '0.98555154', '1.107941', '0.99055797', '0.98876905']})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.8845, 'nll': 0.6721379786729813}, 'chosen_samples': [59430, 37974, 1834, 6466, 51858, 32348, 16540, 12935, 24533, 57463], 'chosen_samples_score': ['0.81995547', '0.8230347', '0.82391864', '0.82494015', '0.8351628', '0.876685', '0.8555847', '0.8407131', '0.85505074', '0.903798']})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.8902, 'nll': 0.6467362910509109}, 'chosen_samples': [5194, 20959, 46088, 30111, 52866, 43212, 35232, 13038, 33035, 7402], 'chosen_samples_score': ['0.8938476', '0.8969544', '0.91136736', '0.9309681', '0.9314927', '0.93069786', '0.9158774', '0.95824736', '0.9802386', '0.9917206']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9153, 'nll': 0.5625604540109634}, 'chosen_samples': [10049, 11711, 3522, 11482, 43636, 54794, 57403, 22034, 2980, 55774], 'chosen_samples_score': ['0.9631234', '0.96514285', '0.9654837', '0.9685271', '0.9724305', '0.9911034', '1.0074403', '0.99386036', '0.9832285', '0.9827671']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9171, 'nll': 0.5728501290082931}, 'chosen_samples': [40732, 31094, 42945, 40398, 5295, 15191, 33784, 9118, 55128, 21700], 'chosen_samples_score': ['0.926872', '0.9270714', '0.9421035', '0.9480896', '0.9506346', '0.95406795', '0.96202195', '0.99855024', '0.98668104', '1.0057895']})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9171, 'nll': 0.5732743918895722}, 'chosen_samples': [47741, 14305, 20036, 17478, 30322, 41501, 2148, 14656, 34771, 59712], 'chosen_samples_score': ['0.997979', '0.9981018', '1.0364729', '1.0724698', '1.0625522', '1.0578766', '1.0451188', '1.0535629', '1.0188932', '1.0235813']})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9388, 'nll': 0.4442838907241821}, 'chosen_samples': [4822, 39877, 20641, 42078, 49487, 26865, 51234, 25462, 32427, 33162], 'chosen_samples_score': ['1.0089736', '1.0100048', '1.0148299', '1.090815', '1.0711815', '1.0703218', '1.0995073', '1.0227842', '1.0512065', '1.0300996']})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9373, 'nll': 0.4517165422439575}, 'chosen_samples': [57972, 53324, 37347, 51432, 19362, 38052, 34328, 26358, 4797, 3070], 'chosen_samples_score': ['0.951966', '0.96552515', '0.96149164', '0.95537525', '0.9750929', '1.0199745', '1.0367327', '0.98606694', '1.0686123', '1.0471983']})
store['iterations'].append({'num_epochs': 11, 'test_metrics': {'accuracy': 0.9381, 'nll': 0.46291079074144365}, 'chosen_samples': [35401, 53854, 19868, 59681, 14100, 21608, 52808, 22139, 18102, 2803], 'chosen_samples_score': ['1.0338914', '1.0387576', '1.0403409', '1.0557674', '1.1013741', '1.0939875', '1.1521261', '1.0569036', '1.0696476', '1.0609477']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9334, 'nll': 0.4615109100937843}, 'chosen_samples': [49286, 37132, 14062, 3273, 34188, 10044, 7438, 34520, 59747, 46368], 'chosen_samples_score': ['1.0142976', '1.0158764', '1.0199906', '1.0221971', '1.0260999', '1.051074', '1.0453727', '1.107695', '1.1718313', '1.034404']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9381, 'nll': 0.42418154031038285}, 'chosen_samples': [32507, 17389, 55906, 59741, 44202, 49890, 33388, 45954, 21636, 27646], 'chosen_samples_score': ['0.9529782', '0.9546301', '0.957233', '0.97091216', '0.9973229', '1.0008703', '1.0274739', '1.0083563', '1.05095', '1.1449335']})
store['iterations'].append({'num_epochs': 12, 'test_metrics': {'accuracy': 0.9453, 'nll': 0.4125694289803505}, 'chosen_samples': [45446, 41602, 50274, 6980, 46573, 52910, 36072, 28930, 42199, 34708], 'chosen_samples_score': ['1.009906', '1.0249618', '1.0592846', '1.0192004', '1.0120416', '1.0136799', '1.0595663', '1.062489', '1.1344147', '1.0730861']})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9454, 'nll': 0.3982634484767914}, 'chosen_samples': [54030, 44753, 26511, 16836, 13183, 32994, 8235, 54646, 1674, 42703], 'chosen_samples_score': ['0.88089794', '0.8840808', '0.8960312', '0.8990151', '0.89393306', '0.9409914', '0.89192384', '0.9469073', '1.0298327', '1.075788']})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9453, 'nll': 0.4000979870557785}, 'chosen_samples': [46413, 14286, 26266, 47260, 41639, 24587, 1518, 51863, 32776, 17053], 'chosen_samples_score': ['0.8742055', '0.8757892', '0.891246', '0.92283934', '0.89298993', '1.0567453', '0.8939729', '0.88605046', '0.9788943', '0.90465164']})
store['iterations'].append({'num_epochs': 11, 'test_metrics': {'accuracy': 0.952, 'nll': 0.3792871430516243}, 'chosen_samples': [24504, 23490, 33824, 10503, 59286, 50320, 12305, 59294, 23086, 51337], 'chosen_samples_score': ['0.96912456', '0.9814981', '0.99560136', '0.9998261', '0.99991405', '1.0332812', '1.0087022', '1.0424821', '1.014734', '1.0425255']})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9506, 'nll': 0.3940142720937729}, 'chosen_samples': [22272, 37161, 35406, 23956, 22742, 30214, 20110, 36065, 21990, 1075], 'chosen_samples_score': ['0.8534742', '0.85514', '0.8551653', '0.8595699', '0.86434084', '0.89754385', '0.93847406', '0.8884566', '0.9660821', '0.87706727']})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.951, 'nll': 0.3796312794089317}, 'chosen_samples': [20002, 18473, 56006, 57441, 15832, 43043, 10886, 14793, 57524, 44698], 'chosen_samples_score': ['0.8368684', '0.8677492', '0.919927', '0.87563723', '0.88782257', '0.8684879', '0.9798301', '0.8473993', '0.8537928', '0.8623934']})
store['iterations'].append({'num_epochs': 13, 'test_metrics': {'accuracy': 0.96, 'nll': 0.3408845618367195}, 'chosen_samples': [15913, 54195, 6347, 41924, 23886, 53872, 2450, 6269, 37469, 53496], 'chosen_samples_score': ['0.99106634', '1.0027485', '1.0338323', '1.0333087', '1.0366445', '1.0395105', '1.0567455', '1.0676868', '1.0787485', '1.0700035']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9562, 'nll': 0.3485205680131912}, 'chosen_samples': [24038, 47914, 5103, 39778, 41233, 52169, 49354, 50308, 47479, 55244], 'chosen_samples_score': ['0.8995482', '0.9015411', '0.9068784', '0.9334004', '0.93704873', '0.92461616', '0.91107917', '0.91343147', '0.91200763', '0.91927755']})
store['iterations'].append({'num_epochs': 11, 'test_metrics': {'accuracy': 0.9558, 'nll': 0.34714253395795824}, 'chosen_samples': [39576, 44927, 14722, 24078, 21880, 53507, 39561, 50090, 5013, 5790], 'chosen_samples_score': ['0.89861524', '0.8991586', '0.90750414', '0.9245888', '0.97322685', '0.9246733', '1.0565612', '0.91462', '0.95535254', '0.92330724']})
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store['iterations'].append({'num_epochs': 12, 'test_metrics': {'accuracy': 0.9614, 'nll': 0.34696798026561737}, 'chosen_samples': [22716, 26101, 42933, 32668, 10992, 31664, 26186, 41464, 53976, 5740], 'chosen_samples_score': ['0.9342631', '0.9344602', '0.9415333', '1.1132213', '0.95728564', '0.98894864', '0.94191086', '0.9602888', '0.993002', '0.94210994']})
store['iterations'].append({'num_epochs': 13, 'test_metrics': {'accuracy': 0.9546, 'nll': 0.39727577567100525}, 'chosen_samples': [18324, 12416, 57837, 25092, 52927, 43745, 10257, 9180, 13259, 33505], 'chosen_samples_score': ['1.0077274', '1.0217966', '1.0513905', '1.0344138', '1.0675809', '1.0515542', '1.0325044', '1.1329182', '1.0251956', '1.0539017']})
store['iterations'].append({'num_epochs': 13, 'test_metrics': {'accuracy': 0.9596, 'nll': 0.3513736426830292}, 'chosen_samples': [55856, 33057, 18654, 4438, 48154, 42437, 31184, 37773, 2706, 34420], 'chosen_samples_score': ['0.9934384', '0.9938762', '1.0046825', '1.0376637', '1.0128629', '1.074992', '1.0195842', '1.032154', '1.0424042', '1.0032408']})
store['iterations'].append({'num_epochs': 12, 'test_metrics': {'accuracy': 0.9644, 'nll': 0.30310001522302626}, 'chosen_samples': [41426, 892, 38329, 49573, 32047, 44095, 53997, 54880, 59314, 49192], 'chosen_samples_score': ['0.92449975', '0.930089', '0.93178755', '0.9354847', '0.9751318', '0.9689644', '0.9924478', '0.9550281', '1.0274785', '1.0080073']})
store['iterations'].append({'num_epochs': 15, 'test_metrics': {'accuracy': 0.9657, 'nll': 0.29755626767873766}, 'chosen_samples': [17603, 23482, 50431, 32573, 30692, 224, 38920, 28357, 17382, 22531], 'chosen_samples_score': ['0.99817216', '1.050318', '1.0525069', '1.0063746', '1.011979', '1.0527606', '1.0774295', '1.0873432', '1.0628426', '1.0608525']})
store['iterations'].append({'num_epochs': 14, 'test_metrics': {'accuracy': 0.9663, 'nll': 0.30813067108392717}, 'chosen_samples': [21705, 7768, 32335, 42438, 57031, 38171, 30818, 45502, 27254, 25835], 'chosen_samples_score': ['0.9824921', '0.98670465', '1.0627577', '1.086267', '1.0139365', '1.0144141', '1.0855372', '1.0517601', '1.0321305', '0.99934375']})
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store['iterations'].append({'num_epochs': 14, 'test_metrics': {'accuracy': 0.969, 'nll': 0.2984517827630043}, 'chosen_samples': [53036, 59526, 33318, 35688, 20169, 55792, 15412, 31016, 58832, 18501], 'chosen_samples_score': ['0.9607245', '0.9985407', '1.0234916', '1.0046644', '1.072182', '0.99591357', '0.9702059', '0.9758136', '1.0500996', '0.9609903']})
store['iterations'].append({'num_epochs': 12, 'test_metrics': {'accuracy': 0.9722, 'nll': 0.2757512733340263}, 'chosen_samples': [24278, 28246, 9677, 26605, 53906, 59731, 49905, 1352, 7638, 45056], 'chosen_samples_score': ['0.8844544', '0.8958771', '0.9030097', '0.9260971', '0.964068', '0.99114764', '0.912013', '0.9050415', '0.9442325', '1.0034195']})
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store['iterations'].append({'num_epochs': 15, 'test_metrics': {'accuracy': 0.9762, 'nll': 0.2669247329235077}, 'chosen_samples': [25300, 38389, 17005, 40704, 28632, 42415, 35938, 14866, 22743, 50714], 'chosen_samples_score': ['0.9545444', '0.96441567', '0.9554511', '0.9750247', '0.9789083', '1.015345', '0.9762108', '1.0539589', '1.0560389', '1.023596']})
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store['iterations'].append({'num_epochs': 21, 'test_metrics': {'accuracy': 0.9851, 'nll': 0.2064064085483551}, 'chosen_samples': [49923, 42504, 35482, 27292, 47445, 13386, 5600, 670, 3030, 48102], 'chosen_samples_score': ['0.83207667', '0.8379072', '0.8575118', '0.83899164', '0.85548484', '0.861827', '0.8992141', '0.8783276', '0.8813524', '0.87648386']})
store['iterations'].append({'num_epochs': 23, 'test_metrics': {'accuracy': 0.9832, 'nll': 0.18611237034201622}, 'chosen_samples': [14144, 25508, 26516, 41113, 50355, 22832, 55739, 38315, 18196, 37552], 'chosen_samples_score': ['0.8129578', '0.81625944', '0.82220805', '0.831881', '0.87712985', '0.8463337', '0.85556203', '0.8320425', '0.91561675', '0.84040916']})
store['iterations'].append({'num_epochs': 30, 'test_metrics': {'accuracy': 0.9825, 'nll': 0.20048719123005868}, 'chosen_samples': [53098, 20976, 44484, 39873, 29360, 20663, 52963, 50946, 52949, 32968], 'chosen_samples_score': ['0.92339754', '0.9254642', '0.9361415', '1.021192', '0.9463738', '1.0090363', '0.93694043', '0.95762604', '0.9476657', '0.9415593']})
store['iterations'].append({'num_epochs': 19, 'test_metrics': {'accuracy': 0.9868, 'nll': 0.1790689766407013}, 'chosen_samples': [52130, 3094, 470, 2622, 26184, 55278, 40268, 31742, 1270, 23252], 'chosen_samples_score': ['0.7885206', '0.7915069', '0.7964834', '0.7974224', '0.7980424', '0.8119595', '0.82204634', '0.80161476', '0.8084992', '0.8612895']})
store['iterations'].append({'num_epochs': 20, 'test_metrics': {'accuracy': 0.9867, 'nll': 0.17698339372873306}, 'chosen_samples': [54892, 42355, 45616, 52694, 30062, 42673, 44338, 25220, 17542, 13680], 'chosen_samples_score': ['0.79130214', '0.8102267', '0.80103153', '0.8109962', '0.81226146', '0.8708337', '0.83935714', '0.8771655', '0.8447774', '0.8125861']})
